Introduction and Course Overview
Spring 2026
(How) can state-of-the-art methods improve financial decision making?
Empirical finance
Data Science
At the end of the course, you should
Guided coding assignments
The lecture is based on very recent academic papers
AEF closes the gap between core finance courses at KU
Related courses to consider
Lecture hall
The learning curve is very steep, remember to reach out in case something is unclear
https://padlet.com/stefanvoigt2/8xyqpu91evwvndss
I stick to R or Python (or Julia) because
Less of an issue
In case you did not set your environment up yet, follow the Technical Prerequisite section in Absalon:
If you have never used R, Chapters 1-3 of this book are an excellent starting point: Basic introduction to R
Sign up for a free account at DataCamp (only with econ.ku.dk or alumni.ku.dk address)
pandas, numpy, plotninetidyverse and tidyfinanceread_csv(), read_txt(), .., or download with tidyfinancepandas, numpy, and tidyfinancepd.read_csv(), pd.read_txt(), .., or download with tidyfinance|> and .verb(subject, complement) is replaced by subject |> verb(complement) (R) or subject.verb(complement) (Python)%>% instead of |>!date formatcount, group_by and summarise solve many data science questionssize, groupby and summarise solve many data science questionsggplot2: Grammar of graphics differentiates between the data and the representationfrom plotnine import *
from mizani.formatters import dollar_format
price_plot = (
ggplot(prices, aes(x="date", y="volume_usd", color="symbol"))
+ geom_point(size=0.2)
+ geom_line(linetype="dotted")
+ labs(
x="Year",
y="Volume (USD)",
title="Daily volume",
color=None
)
+ facet_wrap("~symbol", scales="free_x")
+ theme_bw()
+ scale_y_continuous(labels=dollar_format(suffix="M", scale=1e-6))
)
price_plot.show()Core principles to make code readable
|> or .)trading_volume_usd instead of tmp_Var_2)Quarto# A tibble: 6,548 × 3
symbol date ret
<chr> <date> <dbl>
1 AAPL 2000-01-04 -0.0843
2 AAPL 2000-01-05 0.0146
3 AAPL 2000-01-06 -0.0865
4 AAPL 2000-01-07 0.0474
5 AAPL 2000-01-10 -0.0176
6 AAPL 2000-01-11 -0.0512
7 AAPL 2000-01-12 -0.0600
8 AAPL 2000-01-13 0.110
9 AAPL 2000-01-14 0.0381
10 AAPL 2000-01-18 0.0348
# ℹ 6,538 more rows
symbol date ret
1 AAPL 2000-01-04 -0.084310
2 AAPL 2000-01-05 0.014633
3 AAPL 2000-01-06 -0.086538
4 AAPL 2000-01-07 0.047369
5 AAPL 2000-01-10 -0.017588
6 AAPL 2000-01-11 -0.051151
7 AAPL 2000-01-12 -0.059973
8 AAPL 2000-01-13 0.109677
9 AAPL 2000-01-14 0.038114
10 AAPL 2000-01-18 0.034848
Stick to the following roadmap if you encounter problems
download_data() is doing, type ?download_data (R) or ?tf.download_data (Python) in the consoleChatGPT and Github Copilot